AgentOps
Observability for AI agents — session replay, cost and latency tracking, and debugging for multi-step runs.
AgentOps is observability built for agents specifically: session replay of every step, tool call, and LLM call, plus cost, latency, and failure tracking. A few lines of SDK turn an opaque multi-step agent run into a timeline you can debug and a dashboard you can monitor.
AgentOps is an observability platform built specifically for AI agents. Agents are uniquely hard to debug — one request fans out into a tree of LLM calls, tool calls, and decisions — and AgentOps turns that opacity into a session replay: a step-by-step timeline of everything the agent did, with cost, latency, and errors attached.
It is aimed at developers running agents in development or production who need to see why a run went wrong, what it cost, and where it slowed down. It integrates with the major agent frameworks with minimal setup.
Highlights
- Session replay — a full timeline of LLM calls, tool calls, and steps for any agent run.
- Cost & latency tracking — per-run and aggregate spend and timing, so regressions and runaway loops surface fast.
- Failure analytics — catch errors, dead-ends, and repeated tool failures across runs.
- Framework integrations — drop-in support for popular agent frameworks (CrewAI, AutoGen, OpenAI Agents SDK, LangGraph, and more).
- Lightweight SDK — a couple of lines to start capturing sessions.
In an AI-assisted workflow
import agentops
agentops.init() # then run your agent — every step, tool call, cost, and error is capturedTIP
Pair agent-specific replay (AgentOps) with general LLM observability (Langfuse, Arize Phoenix) depending on whether you're debugging the agent's control flow or the underlying model calls.
Good to know
AgentOps offers an open-source SDK with a hosted dashboard on a freemium model (free tier plus paid plans for scale and retention). You bring your agent framework and model provider. It's most useful once an agent has enough steps that logs alone stop being readable — see agent-reliability-reviewer for hardening what the traces reveal.
Related
- Agent Reliability ReviewerUse this agent to make an AI agent production-ready — reviewing its loops, cost controls, error handling, tool use, human-in-the-loop gates, checkpointing, and observability, then reporting concrete failure modes and fixes. Examples — "is our agent safe to ship?", "our agent loops forever / burns tokens, harden it", "add guardrails and recovery before we put this agent in front of users".
- LangfuseAn open-source LLM engineering platform for tracing, evals, prompt management, and metrics.
- Arize PhoenixAn open-source LLM observability and evaluation tool built on OpenTelemetry, runnable anywhere.
- Which Agent Framework in 2026? LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK vs Claude Agent SDKA decision guide to the major AI agent frameworks — control vs. abstraction, multi-agent models, state and durability, and which fits your project.